Conventionally, in stopping a train at a station, it is required to stop a car precisely at a target spot such as a station by controlling a brake force generating device such as a regenerative brake by a motor and an air brake using frictional force. Further, in brake control of a train, it is also required to stop it precisely at a target spot without damaging ride quality. However, it takes a very high skill to stop a train precisely at a target spot by operation of a driver. Thus, in these years, automatic control devices such as an automatic train operation (ATO) and a train automatic stop-position controller (TASC) are being put in for use.
In train automatic control, because the accuracy of the stop position varies due to variation in brake characteristics and variation in train resistance that is an external factor, first the brake characteristics and train resistance of the train should be considered. Here, the train resistance is the sum of running resistance, curvature resistance, and gradient resistance. As the brake characteristics, there can be cited a dead time that is a response time until brake force occurs and a first-order lag, variation in a mechanical friction characteristic that is variation in the deceleration characteristic of a train at the time of braking while running, the influence of a hysteresis characteristic of brake pressure, the influence of weather, wind speed, and a gradient that is variation in the speed characteristic due to environmental factors, and the influence of age deterioration and changes over time in these. As obvious from dynamics representing relations between force, mass, and acceleration, deceleration varies depending on car weight even if the same brake force is applied. That is, the brake characteristics are subject to a modeling error and model fluctuation, i.e., variation. In order to deal with these factors which affect the brake characteristics, a technique has been proposed which uses, in combination, a means of planning a brake pattern based on a prediction model created beforehand from data about previous runs for the train to run and a means of performing model estimation using spot information from transponders and speed information from a speed electric generator as data about actual runs for improving prediction accuracy so as to improve the accuracy of the prediction model.
Although increase/decrease in the number of passengers causes variation in the mass of the train, a variable load device need only be installed to deal with variation in the mass of the train due to increase/decrease in the number of passengers. The variable load device is one which adjusts brake force in response to increase/decrease in the load, thereby adjusting deceleration according to a specifying value to be constant regardless of the mass of the train.
For example, Patent Literature 1 addresses “performing deceleration control with good responsiveness and followingness at the stage of deceleration control before going into position control for aligning stop position”. Patent Literature 1 discloses an automatic train operation device where “An object system deducing unit 40, taking into account the dead time of the object system estimated by a dead time estimating unit 50, deduces the object system of a fixed-position stop control system. A control parameter computing unit 70 computes control parameters to match the object system taking into account that dead time. A principle control unit 60 performs deceleration control using the control parameters, so that the fixed-position stop control is possible with the dead time of the object system being taken into account”. In Patent Literature 1, with the dead time, load weight, and weather out of the brake characteristics being taken into account, variation in the brake characteristics is dealt with by estimating the dead time of the object, controlling with use of a Smith dead time compensator utilizing the estimated dead time, and by using control parameter settings in which the weather and load weight have been taken into account.
Further, for example, Patent Literature 2 addresses “making the stop position error small even when an error exists in the prediction model of the automatic train operation device”. Patent Literature 2 discloses an automatic train operation device where “A stopping plan is created at the start of fixed-position stop control and is reviewed periodically with as short a period as a control period while the train is running, thereby killing the influence of the error in the prediction model as much as possible before it becomes large so as to make the stop position error small. In addition, brake-torque estimation is performed to correct the brake torque value of the prediction model, thereby making the prediction model error smaller and thus the stop position error smaller, and in which further if the predicted value of the stop position error is a value of the same order each time the stop position error is predicted based on the stopping plan reviewed during the preceding control period, the stop target position is displaced accordingly in the opposite direction, thereby making the stop position error smaller”. In Patent Literature 2, such a stop pattern as to minimize the error between the planned stop position computed from a model predicted from train behavior data and the planned stop position computed from a current running plan is created based on actual deceleration.